SKILLEMALL.ai
GitHub ↗ RU Sign in

CD wikipedia

Access Wikipedia via MCP — search articles, get summaries, random facts, dinosaur facts, today's featured article, today's historical events, article categories, outgoing links, view counts, current news, and most-read articles. Multi-language support (10 wikis). Great for research, content hooks, and general knowledge lookups.

ClawHub Agent Skills author: Evan790 v1.6.26 MIT-0 8 files · 2 scripts body ≈ 7 925 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

Access Wikipedia via MCP — search articles, get summaries, random facts, dinosaur facts, today's featured article, today's historical events, article…

As a process D 37/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting and documentsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
63
Run on models
none yet
Process rating
D
37/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: wikipedia (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 7925 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 37/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 11 mutating operations with no state check
  • 40Consistency. Frontmatter name (wikipedia) differs from the folder (wiki-mcp-pub2)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7925 tokens
  • 85Steps. 46 steps, 2 vague phrases
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 329: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 46 items
  • +4Has examples (5 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This is a read-only Wikipedia/Wikimedia research skill with broad but disclosed public-data lookup tools, plus privacy and dependency caveats.
LLM: benign (high) · VirusTotal: · 5 Oct 2026